Prediction Mode Research Articles

Most frequent mode for intra‐mode coding in video coding

The emerging versatile video coding (VVC) standard currently adopts 67 intra-prediction modes in order to improve the intra-prediction performance. The most probable mode (MPM) is used in video coding to encode the prediction mode efficiently based on the modes of the neighbouring blocks. Due to an increase in the number of intra-modes and increase in the resolution of the input sequence, it is necessary to efficiently encode the intra-mode of the current block. This Letter proposes an efficient intra-mode coding method extending MPM called most frequent mode (MFM), which exploits the number of occurrences of the modes in the neighbouring blocks. The proposed MFM derives the most frequent intra-mode. Then the derived MFM is signaled by a flag with a higher priority than MPM. Experimental results show that, compared to the test model of VVC (VTM), the proposed method gives up to 0.48% Bjotegaard-Delta-rate savings for the all-intra-configuration.

Discrete wavelet transform-based fast and high-efficient lossless intraframe compression algorithm for high-efficiency video coding

High-efficiency video coding (HEVC) is a very common application in today’s electronics field. Lossless intraframe compression is preferred for many specific applications. Compared to lossy intracompression or interframe encoding, intraframe encoding has lower computation complexity but an unsatisfactory compressed bit-rate. Extant optimization algorithms center on improving coded bits in space domains and few include considerations for accelerating encoding time. We focus on video compression in the frequency domain to reduce bit-rate while saving encoding time. The proposed method involves placing discrete wavelet transform in front of the traditional HEVC tools to transform a given frame into frequency coefficients. To accelerate the encoding process, fast prediction mode skip and CU depth decision are used to bypass certain unnecessary computations based on texture detection results. The experimental results indicated that our method reduces bit-rate by 6.466% on average with 48.068% encoding time savings compared to the original HEVC encoding.

Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC

Three-dimensional extension of the high efficiency video coding (3D-HEVC) is an emerging international video compression standard for multiview video system applications. Similar to HEVC, a computationally expensive mode decision is performed using all depth levels and prediction modes to select the least rate-distortion (RD) cost for each coding unit (CU). In addition, new tools and intercomponent prediction techniques have been introduced to 3D-HEVC for improving the compression efficiency of the multiview texture videos. These techniques, despite achieving the highest texture video coding efficiency, involve extremely high-complex procedures, thus limiting 3D-HEVC encoders in practical applications. In this paper, a fast texture video coding method based on motion homogeneity is proposed to reduce 3D-HEVC computational complexity. Because the multiview texture videos instantly represent the same scene at the same time (considering that the optimal CU depth level and prediction modes are highly multiview content dependent), it is not efficient to use all depth levels and prediction modes in 3D-HEVC. The motion homogeneity model of a CU is first studied according to the motion vectors and prediction modes from the corresponding CUs. Based on this model, we present three efficient texture video coding approaches, such as the fast depth level range determination, early SKIP/Merge mode decision, and adaptive motion search range adjustment. Experimental results demonstrate that the proposed overall method can save 56.6% encoding time with only trivial coding efficiency degradation.

Load Prediction of Space Deployable Structure Based on FBG and LSTM

To predict the dynamic load of space deployable structure, a novel prediction method based on fiber Bragg grating (FBG) sensors and long short-term memory (LSTM) neural network is proposed. The FBG sensors can measure strain values of the structure. The LSTM establishes the relationship between the strain values and load without a complex dynamic model. The LSTM first works in training mode, its input is strain values and output is the load. Then, the LSTM works in the prediction mode, which takes the current strain values as input and uses the trained network model to predict load. The network of LSTM is simple, which can achieve good prediction effect and shorten the running time. To evaluate the effectiveness of the proposed method, numerical simulations and experiments of space deployable structure with single-point input load are employed. The results demonstrate that compared with the method based on traditional neural network, the max error, root mean square error, and consuming the time of the proposed method are reduced by more than 43%, 21%, and 67%, respectively.

CNN-based Intra-Prediction for Lossless HEVC

The paper proposes a novel block-wise prediction paradigm based on Convolutional Neural Networks (CNNs) for lossless video coding. A deep neural network model which follows a multi-resolution design is employed for block-wise prediction. Several contributions are proposed to improve neural network training. A first contribution proposes a novel loss function formulation for an efficient network training based on a new approach for patch selection. Another contribution consists in replacing all HEVC-based angular intra-prediction modes with a CNN-based intra-prediction method, where each angular prediction mode is complemented by a CNN-based prediction mode using a specifically trained model. Another contribution consists in an efficient adaptation of the CNN-based intra-prediction residual for lossless video coding. Experimental results on standard test sequences show that the proposed coding system outperforms the HEVC standard with an average bitrate improvement of around 5%. To our knowledge, the paper is the first to replace all the traditional HEVC-based angular intra-prediction modes with an intra-prediction method based on modern Machine Learning techniques for lossless video coding applications.

Research and Implementation of Fast Algorithm for Intra Prediction Mode Selection Oriented to Hardware

Research and Implementation of Fast Algorithm for Intra Prediction Mode Selection Oriented to Hardware

A Compact 32-Pixel TU-Oriented and SRAM-Free Intra Prediction VLSI Architecture for HEVC Decoder

In the High Efficiency Video Coding (HEVC), a variety of CU sizes and intra prediction modes significantly improve coding efficiency, but also bring higher computational complexity. This paper proposes a new compact VLSI architecture for HEVC intra prediction, which is geared towards 8K video decoding. It supports all the transform unit (TU) sizes and 35 HEVC intra prediction modes. First, this paper introduces a TU-oriented intra predictor with a throughput of 32 pixels, which can be newly arranged with the TU size. It can be a line of 32 pixels, two lines of 16 pixels, 4 lines of 8 pixels or four lines of four pixels. This TU-oriented architecture allows intra-prediction and inverse discrete cosine transform (IDCT) to be computed in parallel, removing the memory between them. In addition, a horizontal and vertical line buffer for reference sample is proposed, which only cost 0.8K bit and is implemented by register files with SRAM-free. Finally, to further reduce hardware consumption, multipliers can be shared in the prediction. The implementation result shows that the compact architecture supports 8K video application and costs 66.2K logic gates, which is synthesized with the TSMC 65nm process under 400MHz.

基于预测单元尺寸的高效视频编码帧内预测模式快速选择的改进算法

基于预测单元尺寸的高效视频编码帧内预测模式快速选择的改进算法

Fast Depth Map Intra Coding for 3D Video Compression Based Tensor Feature Extraction and Data Analysis

3D high-efficiency video coding (3D-HEVC) is the latest standard for 3D video compression created by the ISO/IEC MPEG and ITU-T Video Coding Experts Group (VCEG) based on a new video format called multiview texture videos plus depth maps (MVDs). In 3D-HEVC depth map intra prediction, the test model uses the conventional HEVC intra modes and new supplementary coding tools called depth modeling modes (DMMs) to better preserve the sharp edges of depth maps. These intra prediction modes fundamentally enhance the depth map intra coding efficiency. Although this process enables high coding efficiency, it also results in a very high encoding complexity, which limits the use of the 3D-HEVC encoder in practical and real-world applications. For most cases, the conventional intra coding and DMMs can be skipped if the current region is classified as a homogenous region. Bringing this intuitive approach to practice, this paper proposes fast depth map intra coding based on tensor feature extraction and data analysis. The experimental results show that the proposed intra model decision brings a good complexity reduction with negligible loss of rate distortion performance.

A Cover Selection HEVC Video Steganography Based on Intra Prediction Mode

Existing video steganography puts much emphasis on the design of algorithms such as mapping rules or distortion functions, thereby ignoring the selection of cover to embed secret information. However, this is just one of the major differences between image steganography and video steganography. In addition, since HEVC is the latest standard in the video codec field, it is of important academic significance and applied value to study HEVC-based steganography. This paper proposes a novel video steganography in HEVC, based on intra-prediction mode (IPM). Firstly, this paper analyzes the probability distribution of 4 × 4 IPMs. Then a cover selection rule combined with the Coding Unit (CU) and Prediction Unit (PU) coding information is proposed, which can improve the security performance of a stego video stream. In addition, matrix coding is used as a coding example to implement the steganography on HEVC video streams. Experimental results show that the proposed algorithm can not only maintain the video quality and the security performance but is also easy to implement. Furthermore, the proposed cover selection rule can also be integrated into other HEVC IPM based steganography.

On the comparison of urban canopy effects parameterisation

To assess the impact of cities on climate, modelling is used with inclusion of urban parameterisation which is important when applying higher resolution. It is essential to assess uncertainties of urban effect parameterisations. To estimate this we performed experiments with available settings of urban canopy models over central Europe for 2001-2010. We have used RegCM4 and WRF in 10 km resolution, three surface schemes and five urban canopy parameterisations available. Selected WRF options were tested for urbanised weather prediction in 3 km resolution to show the effect in a single extreme case study. Urban heat island was detected in all simulations, for summer night the strongest on average was a few °C. We demonstrate that for cities like Prague it can in extremes approach 5°C, for larger city such as Budapest it is 8°C. In weather prediction mode, WRF model captured these effects in a single heat wave with selected parameterisations well.

A fuzzy-based decision support system for soil selection in olericulture

With the advent of modern computer technology, the field of Artificial Intelligence is playing a significant role in improving almost every spectrum of human life. In the field of agriculture, there is always need for optimality with improved crop yield. This paper dwells majorly on the application of fuzzy logic to predict crop type with optimal crop yield based on available soil nutrients. Some soil data samples were collected from the department of soil science, Federal University of Agriculture, Abeokuta and used as input into the system. The proposed system was simulated using MatLab Fuzzy inference System with a triangular member function. The range of nutrients was later deployed as input into a visual basic developed application to predict the best crop to be planted. A dual method (static and dynamic) was used in testing and validating the result of research which showed a significant improvement on the crop type selection than the conventional prediction mode.

Exploiting Data Mining for Fast Inter Prediction Mode Decision in HEVC

HEVC has made significant progress in coding efficiency. Compared with the previous generation H.264/AVC video coding standard, HEVC can save 50% of code rate under the same visual quality. However, this drop in bit rate is accompanied by an increase in computational complexity, which poses a great challenge to the application and development of HEVC. After analyzing the great correlation between the partitioning mode of coding unit in the encoder and several parameters, the partitioning problem of the CU is modeled as a classification problem in data mining, and it is decided to use a decision tree to solve this classification problem. We implement the decision tree on the official software HM16.2 and test the algorithm on the test set. Experiments show that fast algorithm based on decision tree can improve the encoding speed effectively with little influence on the encoding performance.

Fast prediction for quality scalability of High Efficiency Video Coding Scalable Extension

In response to the increased demand for high-resolution video, the new generation of video standards, High Efficiency Video Coding (HEVC) and its scalable extension (SHVC) have been finalized. The compression of HEVC/SHVC is efficiently improved and supports ultra-high resolution (UHD). Therefore, the coding complexity of HEVC/SHVC is much higher than those of previous standards. The framework of SHVC is based on HEVC and is divided into several types of scalable video. SHVC can be decoded into various video resolutions, frame rates and qualities, and only needs to be encoded once, but with higher complexity than HEVC. Thus, how to reduce the coding complexity of SHVC is the purpose of this paper. Our proposed algorithm accelerates the enhancement layer (EL) prediction by utilizing encoded Coding Unit (CU) sizes, prediction modes, motion vectors and Rate-Distortion Costs (RD-Costs) of the base layer (BL) and encoded CU sizes of the enhancement layer for quality scalability of SHVC. Experimental results show that the proposed algorithm can save lots of time while maintaining good video quality, and the performance is better than those of previous works.

New Version of Adaptive Speed Observer based on Neural Network for SPIM

This paper presents a novel Stator Current based Model Reference Adaptive System (SC_MRAS) speed observer for high-performance Six Phases Induction Motor (SPIM) drives using linear neural network. The article aim is intended to improve performance of an SC_MRAS observer, which were presented in the literature. In this proposed scheme, the measured stator current components are used as the reference model of the MRAS observer to avoid the use of a pure integrator and reduce the influence of motor parameter variation. The adaptive model uses a two-layer Neural Network (NN) to estimate the stator current, which has been trained online by means of a Least Squares (LS) algorithm instead of uses a nonlinear Back Propagation Network (BPN) algorithm to reduce the complexity and computational burden, it also help to improve some disadvantages cause by the inherent nonlinearity of the BPN algorithm as local minima, two heuristically chosen parameters, initialization, and convergence problems, paralysis of the neural network. The adaptive model of the proposed scheme is employed in prediction mode, not in simulation mode as is usually the case in the literature, this made the proposed observer operate better accuracy and stability. In the proposed observer, stator and rotor resistance values are estimated online, these values thereafter were updated for the current observer and rotor flux identifier to enhance the accuracy, robustness and insensitivity to parameters variation for the proposed observer. The proposed LS SC_MRAS observer has been verified thought the simulation and compared with the BPN MRAS observer. The simulation results have proven that the speed is estimated a consequent quicker convergence, do not need the estimated speed filter, lower estimation errors both in transient and steady state operation, better behavior in low and zero speed operation.

Fast Integer Motion Estimation With Bottom-Up Motion Vector Prediction for an HEVC Encoder

Although advanced motion vector prediction (AMVP) modes based on motion estimation (ME) are selected significantly less due to the merge mode newly adopted in the high-efficiency video coding (HEVC), integer ME (IME) still occupies a large amount of computation in HEVC because the HEVC supports a highly flexible block partitioning structure. Introduction of the merge mode in HEVC substantially affects the optimal search algorithm of IME. Therefore, this gives a chance to reduce the computational complexity of ME with marginal drop of the compression efficiency. This paper proposes a new IME algorithm that significantly reduces its search ranges. Computational complexity of the proposed algorithm is reduced without serious degradation in coding efficiency by obtaining additional accurate motion vector predictors (MVPs) in a bottom-up order and searching narrow regions around multiple MVPs. These bottom-up MVPs are obtained from the prediction units (PUs) in the coding units (CUs) in the hierarchically lower level, which share the pixel area either completely or partially with the current PU. Although the encoder should perform the proposed IME in a bottom-up order from the smallest CUs to the larger CUs, several other processes, such as fractional ME and merges mode are performed in a top-down order to exploit several fast algorithms already adopted in the HEVC test model encoder. To keep compatibility with HEVC standard, the bottom-up MVPs are utilized for only IME. The bitstream is generated using standard AMVP. According to the simulation results, it is confirmed that the proposed algorithm improves the accuracy of MVPs, which leads to the reduction of IME computational complexity up to 81.95% on average with the Bjontegaard delta bitrate of 0.39%.

Fast 3D-HEVC inter mode decision algorithm based on the texture correlation of viewpoints

To reduce the computational complexity of 3D-HEVC, a fast inter mode decision algorithm based on texture correlation of adjacent viewpoints is proposed (FMD_inter). By studying the statistical probability of inter prediction mode distributions in 3D-HEVC coding scheme, the mode of dependent view is highly correlated with the base one, which enables the early mode decision. Moreover, by analyzing the relationship between video texture and the inter prediction mode, selectively skip checking symmetric motion partition (SMP) mode and asymmetric motion partition (AMP) mode according to the texture features of coding unit is reasonable. The proposed algorithm is able to achieve a reduction of computational complexity by 18.71% on average, while maintaining the coding efficiency. The subjective quality of the synthesized views also shows the effectiveness of the algorithm.

Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes

For a mobile robot, navigation skills that are safe, efficient, and socially compliant in crowded, dynamic environments are essential. This is a particularly challenging problem as it requires the robot to accurately predict pedestrians’ movements, analyse developing traffic situations, and plan its own path or trajectory accordingly. Previous approaches still exhibit low accuracy for pedestrian trajectory prediction, and they are prone to generate infeasible trajectories under complex crowded conditions. In this paper, we develop an improved socially conscious model to learn and predict a pedestrian’s future trajectory. To generate more efficient and safer trajectories in a changing crowed space, an online path planning algorithm considering pedestrians’ predicted movements and the feasibility of the candidate trajectories is proposed. Then, multiple traffic states are defined to guide the robot finding the optimal navigation strategies under changing traffic situations in a crowded area. We have demonstrated the performance of our approach outperforms state-of-the-art approaches with public datasets, in low-density and simulated medium-density crowded scenarios.

A 4K/60p HEVC Real-Time Encoding System With High Quality HDR Color Representations

This paper describes a 4K/60p high efficiency video coding (HEVC, the latest coding standard) real-time encoding system with high quality high dynamic range (HDR) color representations. The HDR technology provides attractive video contents with a wider dynamic range of luminance, which can make shadows and brighter details appear clearly without any blown out highlights or blocked up shadows. We propose a new rate control method for HDR video coding. The method consists of two algorithms for improving perceptual quality by reducing degradation of HDR-specific color artifacts. The first one, adaptive block size and prediction mode decision, is conducted to suppress perceptual degradation due to prediction error in high chroma areas. The second, local quantization parameter control, is carried out to improve the visual quality with appropriate bit allocation in low chroma areas. The method was implemented in an HEVC real-time encoding system and its performance was assessed by measuring the color difference metrics by 3.86% at the most degraded frame, while peak signal-to-noise ratio remained almost the same. The subjective quality of the decoded HDR images was improved by reducing color artifacts.

Probability Model-Based Early Merge Mode Decision for Dependent Views Coding in 3D-HEVC

As a 3D extension to the High Efficiency Video Coding (HEVC) standard, 3D-HEVC was developed to improve the coding efficiency of multiview videos. It inherits the prediction modes from HEVC, yet both Motion Estimation (ME) and Disparity Estimation (DE) are required for dependent views coding. This improves coding efficiency at the cost of huge computational costs. In this article, an early Merge mode decision approach is proposed for dependent texture views and dependent depth maps coding in 3D-HEVC based on priori and posterior probability models. First, the priori probability model is established by exploiting the hierarchical and interview correlations from those previously encoded blocks. Second, the posterior probability model is built by using the Coded Block Flag (CBF) of the current coding block. Finally, the joint priori and posterior probability model is adopted to early terminate the Merge mode decision for both dependent texture views and dependent depth maps coding. Experimental results show that the proposed approach saves 45.2% and 30.6% encoding time on average for dependent texture views and dependent depth maps coding while maintaining negligible loss of coding efficiency, respectively.